Learning-by-Doing, Learning-by-Exporting, and Productivity: Evidence
from Colombia*
Ana M. Fernandes Alberto E. Isgut
The World Bank Wesleyan University
Abstract: The empirical evidence on whether participation in export markets increases plant-
level productivity has been inconclusive so far. We explain this inconclusiveness by drawing on
Arrow's (1962) characterization of learning-by-doing, which suggests focusing on young plants
and using measures of export experience rather than export participation. We find strong
evidence of learning-by-exporting for young Colombian manufacturing plants between 1981 and
1991: total factor productivity increases 4%-5% for each additional year a plant has exported,
after controlling for the effect of current exports on total factor productivity. Learning-by-
exporting is more important for young than for old plants and in industries that deliver a larger
percentage of their exports to high-income countries.
Key Words: learning, trade, total factor productivity, exports, export-led growth
JEL Classification: D21, F10, L60
* We thank Eduardo Engel, Mike Lovell, and seminar participants at the 2004 Canadian Economic
Association meetings and at Wesleyan University for comments, and Gustavo Hernandez and
Fernando Mesa from Colombia's Dirección Nacional de Planeación for providing Colombia's
input-output matrices. The findings expressed in this paper are those of the authors and do not
necessarily represent the views of the World Bank. Please address correspondence to Ana
Fernandes, Development Research Group, The World Bank, 1818 H Street, N.W., Washington, DC
20433, USA, email: afernandes@worldbank.org.
1. Introduction
Trade and development policies have often been supported by arguments stressing
improvements in productivity at the microeconomic level. The traditional infant industry argument,
for instance, suggests that new firms operate at such high costs that they would be unable to
compete with well-established foreign firms without protection. While such protection would be
detrimental to the country's welfare initially, by allowing domestic firms to start operations it would
give them the opportunity to grow and learn by doing, decreasing production costs over time.
When the infant firms mature, the argument concludes, protection would become unnecessary as
they would be able to compete in international markets.
More recently, policies of export-led growth have also been supported on the grounds that
they improve the productivity of exporting firms. One often-cited reason for such improvement is
that foreign buyers transfer technology to firms that introduce new export products. Additionally, as
case study evidence from Taiwan suggests, the possibility of exploiting profitable opportunities by
selling in export markets may stimulate firms to improve their own technological capabilities
(Westphal (2002)). Improvements in productivity associated with the access to export markets have
been referred to by Clerides et al. (1998) and others as learning-by-exporting.
While the notion that firms learn by exporting is intuitively appealing, the empirical
evidence has been inconclusive. Exporters have been found to be significantly more productive,
larger, more capital-intensive, and to pay higher wages than nonexporters, but these desirable
characteristics might be the cause and not the consequence of their participation in export markets.
If entry into export markets is characterized by economically significant sunk costs, only firms that
1
are productive enough would have the capability of exporting. It is possible, then, that the strong
positive association between productivity and participation in export markets reflects the self-
selection of the better firms into export markets and not the effect of exporting on productivity. In
fact, many empirical studies using plant-level data have found support for this alternative causal
interpretation.1 Yet, self-selection and learning-by-exporting are not mutually exclusive
possibilities, as high-productivity firms that can afford the sunk cost of entry to export markets may,
in principle, continue to improve their productivity as a result of their exposure to exporting.
Several studies have found support for learning-by-exporting, even after controlling for self-
selection effects.2
Despite the large and growing literature on this subject, we believe that the lack of
conclusive evidence on learning-by-exporting warrants further investigation. In this paper, we
revisit a basic question: how to define learning-by-exporting? To answer this question we consider
the parallels between learning-by-exporting and learning-by-doing. In his classical work on
learning-by-doing, Arrow (1962) suggests two main characteristics of learning. First, "learning is
the product of experience. Learning can only take place through the attempt to solve a problem and
therefore only takes place during activity" (p. 155). Second, "learning associated with repetition of
essentially the same problem is subject to sharply diminishing returns... To have steadily increasing
performance, then, implies that the stimulus situations must themselves be steadily evolving rather
than merely repeating" (pp. 155-6).
We believe that Arrow's general characterization of learning applies to domestic firms
1
Bernard and Wagner (1997), Clerides et al. (1998), Bernard and Jensen (1999), Aw et al. (2000), Isgut (2001),
Fafchamps et al. (2002), Delgado et al. (2002), Arnold and Hussinger (2004), Alvarez and Lopez (2004).
2
breaking into export markets. Those firms need to solve new problems such as adopting stringent
technical standards to satisfy more sophisticated consumers. The production of export goods may
require the introduction of new, more efficient equipment to which workers need to adjust. Export
markets are likely to be more competitive than the domestic market, putting pressure on firms to
meet orders in a timely fashion and ensure quality standards for their products. Meeting all these
challenges may help firms improve their productivity. However, once--and provided that--firms
succeed in meeting these challenges, the scope for further learning may be significantly diminished.
This characterization suggests reasons why many previous studies have not found evidence
of learning-by-exporting. One common method to capture learning-by-exporting effects is to
compare the performance of mutually exclusive groups, such as exporters and nonexporters. The
problem is that not all exporters have the same level of engagement in export markets: while some
firms devote considerable resources to their export activities, others are only marginally involved in
exporting, with little scope for learning. The presence of firms marginally engaged in export
markets in the group of exporters is likely to generate a downward bias in the estimated effect of
learning-by-exporting. Another common method is to regress a performance variable, such as total
factor productivity (TFP) or average variable costs, on a lagged indicator variable measuring export
participation. This method is also subject to the criticism that export participation does not capture
the level of engagement in exporting. A further disadvantage is that it does not take into account
how long firms have participated in the export market. If, as Arrow suggested, learning is subject to
sharply diminishing returns, successfully established exporters are unlikely to learn from exporting.
2
Kraay (1999), Castellani (2002), Baldwin and Gu (2003), Van Biesebroeck (2004), Girma et al. (2004), Bigsten et al.
(2004), Hahn (2004), Blalock and Gertler (2004), De Loecker (2004).
3
Therefore, their presence in the group of exporters is also likely to generate a downward bias in the
effect of learning-by-exporting.
The previous discussion suggests two ideas to truly capture learning-by-exporting effects in
the data. A first idea is to focus on young plants. Young plants are much more likely than
established plants to face new stimulus situations, which require managers and workers to find
solutions to new technical and organizational problems. This is the reasoning underlying the strong
evidence of learning-by-exporting found by Delgado et al. (2002) and Baldwin and Gu (2003) for
young Spanish and Canadian manufacturing plants, respectively. In our paper, we seek to confirm
this evidence for young Colombian manufacturing plants. A second idea is to focus on measures of
export experience, rather than on export participation, to capture learning-by-exporting effects. Our
export experience measures, the number of years a firm has exported and an index of cumulative
exports, require that we observe plants' complete production and export histories. This provides us
with a second rationale for focusing on young plants born in 1981 or later as 1981 is the first year
when plant-level export data is available in the annual Colombian manufacturing surveys.
Our results show robust evidence of learning-by-exporting effects for young plants, using
both traditional export participation measures and our export experience measures. The average
annual rate of TFP growth for young entrants into export markets is around 3% to 4% higher than
that for young nonexporters. Each additional year of export experience increases plant TFP
between 4% and 5%, even controlling for a dummy indicating whether the plant is currently
exporting. In extensions to our main results we show that learning-by-exporting is significantly
more important for young plants than for old plants. Also, we find that the relationship between
4
export experience and productivity varies across industries. The volume of industry exports and the
proportion of industry exports going to high-income countries contribute to this cross-industry
variation.
The paper is organized as follows. In Section 2, we review the learning-by-exporting
literature. Our empirical strategy and data are described in Sections 3 and 4. Our results are
presented in Sections 5 and 6. Section 7 concludes.
2. The Measurement of Learning-by-Exporting Effects
Following the influential papers of Bernard and Jensen (1999) and Clerides et al. (1998), the
literature has used two main methods to measure learning-by-exporting effects. The first method
consists of separating the sample into mutually exclusive groups, such as exporters and
nonexporters, to assess differences in plant performance between these groups. Consider the
following equation explaining a measure of performance for firm i at year t:3
lnYit = 0 +1Di0 +2'Zio + 0t + 1Di0t + 2'Zi0t +i +it ,
where Di0 is a dummy variable equal to one if the plant belongs to the "treatment" group and equal
to zero if the plant belongs to the "control" group during the baseline year t = 0; Zi0 is a vector of
observable plant characteristics, such as size or industry affiliation in the baseline year; t is a time
trend; i is an unobservable, time-invariant plant effect; and it is an i.i.d. disturbance. Taking
average annual differences between t = 0 and T, we obtain:
3
We use the terms firms and plants interchangeably in the paper, but our empirical analysis relies on plant-level data.
5
lnYiT (lnYiT -lnYi0)= 0 + 1Di0 + 2'Zi0 +it
1
, (1)
T
where iT iT -i0. The difference-in-difference estimator^1 measures the average differential
in performance between plants in the treatment group and plants in the control group, after
accounting for general trends influencing the performance of all plants equally, trends in
performance related to observable plant characteristics, and unobserved time-invariant plant effects.
Several variants of Equation (1) have been estimated in the literature, using data from both
industrial and developing countries, considering diverse treatment and control groups and time
horizons (T). For example, Bernard and Jensen (1999) define their treatment group as U.S.
manufacturing plants that export in the initial year of the sample, and estimate Equation (1) using
different time periods - 1984-1988, 1989-1992, and 1984-1992 - and different time horizons - short
run (T=1), medium run (T=3, 4), and long run (T=8).
One extension of Equation (1) developed in the literature has been the inclusion of more
than one dummy identifying different, mutually exclusive treatment groups. Bernard and Jensen
(1999), for example, consider three groups: (1) plants that do not export in t=0 but export in t=T
(entrants), (2) plants that export in both years (continuous exporters), and (3) plants that export in
t=0 but do not export in t=T (quitters). The control group consists of plants that do not export in
either year. Another extension developed in the literature has been to select the treatment and
control groups from a subset of the plants in the sample. For example, Aw et al. (2000) use three
nonconsecutive years of plant-level data for Korea and Taiwan and consider (i) regressions where
only entrants to export markets are the treatment group and nonexporters are the control group,
6
excluding quitters and continuous exporters from the estimating sample; and (ii) regressions where
quitters are the treatment group and continuous exporters are the control group, excluding both
entrants and nonexporters from the estimating sample.
While most researchers have estimated some variant of Equation (1) using OLS, Delgado et
al. (2002) use a nonparametric method to compare the distributions of productivity growth of
Spanish manufacturing exporters and nonexporters during 1991-1996. Estimation is performed
separately for subsamples of small and large firms. Interestingly, the authors cannot reject the null
hypothesis that productivity growth is greater for exporters than for nonexporters when the sample
includes only young firms (those that started operations between 1986 and 1991). This result is
valid for small and large young firms.
The most recent innovation in the measurement of learning-by-exporting effects through
group comparisons has been the use of matching methods to control more precisely for differences
between firms in treatment and control groups. As the literature on evaluation methods for
nonexperimental data suggests, the appropriate comparison to evaluate the effects of entry into
export markets involves a counterfactual. Letting YiT1
denote the performance between t=0 and T
of a firm that entered export markets at t = (0 < T) and YiT0
denote the hypothetical
performance of the same firm had it not started to export, the causal effect of entry is captured by
YiT - YiT
1 0
. The average treatment effect on the treated is defined as the expectation of this
counterfactual difference for the subpopulation of firms that actually entered the export market at
time : E[YiT | Di =1] - E[YiT | Di =1].
1 0
Unfortunately, the second term is unobservable.
What we observe is the difference in performance between entrants and nonexporters,
7
E[YiT | Di =1] - E[YiT | Di = 0].
1 0
However, this difference provides a poor estimate of the
causal effect of entering into export markets on performance if, as both theory and empirical
evidence suggest, exporters have very different characteristics from nonexporters. Matching
methods identify a subpopulation of nonexporters that are similar to the population of entrants into
export markets before entry, where similarity is based on set of observable firm characteristics.
Girma et al. (2004), Arnold and Hussinger (2004), and De Loecker (2004) use propensity score
matching to select appropriate subpopulations of nonexporters. This method requires the estimation
of probit regressions to explain the probability of entry into export markets. Once the subpopulation
of nonexporters is identified, differences in performance can be estimated nonparametrically or
through a parametrical form similar to Equation (1) above.
The second method of measurement of learning-by-exporting effects consists of adding one
or more dummies for lagged export participation to a regression explaining a measure of firm
performance. For example, Clerides et al. (1998) regress average variable costs on lagged export
participation controlling for the real exchange rate, lagged capital stock and lagged average variable
costs. Kraay (1999) regresses three alternative measures of performance (labor productivity, TFP,
and unit costs) on lagged export participation, lagged performance and firm fixed effects. Bigsten
et al. (2004) and Van Biesebroeck (2004) estimate production functions with a lagged export
participation dummy added as a shifter of total factor productivity. A representative regression of
the second method is given by:
lnYit = 0 + 1Dit + 2 lnYit + 2' Xit +i +it
-1 -1 , (2)
where Dit-1 is a dummy variable equal to one if the plant exported at time t-1; the vector Xit includes
8
both time-varying variables, such as the capital stock or the number of workers, and fixed plant
characteristics, such as industry affiliation; i is an unobservable, time-invariant firm effect; and it
is a time-varying performance shock.
A major econometric problem with Equation (2) is that the export participation decision is
endogenous because exporting is positively associated with performance; therefore, if it is
persistent, ^1 may pick up the effects of past favorable performance shocks. One way to deal with
this endogeneity problem is to estimate Equation (2) using instrumental variables or GMM. Kraay
(1999) estimates a variant of this equation in first differences under the identifying restrictions that
it is i.i.d. and that the export participation decision is predetermined: E[D is ] = 0 for all s > t.
it
Notice that the latter restriction allows export participation to be positively correlated with current
and past performance shocks, as the self-selection hypothesis suggests. Van Biesebroeck (2004)
also estimates a variant of Equation (2) using Blundell and Bond (1998) System-GMM estimator.
A more involved method of dealing with the endogeneity of export participation is to
estimate Equation (2) simultaneously with another equation explaining the decision to participate in
export markets. This is the approach taken by Clerides et al. (1998), where the two equations are
estimated using full information maximum likelihood. An important finding of Bigsten et al. (2004)
is that this type of estimation result is not robust to the distributional assumption on the error terms.
In Clerides et al. (1998) the four error terms of the model (i.e., one plant effect and one i.i.d.
disturbance in each equation, with the two plant effects and the two i.i.d. errors allowed to be
correlated across equations) are normally distributed. Bigsten et al. (2004) approximate the
bivariate distribution of the two plant effects nonparametrically by a discrete multinomial
9
distribution and find that this modification has dramatic effects on the estimation results: the
coefficient on lagged export participation in their performance equation becomes positive and
significant.4
Table 1 presents a selective overview of the methodology and results of learning-by-
exporting studies. The table focuses only on segments of the cited studies that apply the methods
examined in this section to the study of learning-by-exporting. The numbers (1) and (2) in the
second column correspond, respectively, to studies based on variants of Equations (1) and (2). In
some cases, more than one method is used in the same paper. While the majority of studies use
dummy variables to define the treatment group of exporters or to indicate whether the firm has
exported in an earlier period, there are a few exceptions. Kraay (1999) and Castellani (2002) use
export intensity, defined as the ratio of exports to sales. Interestingly, Castellani (2002) finds
evidence of learning-by-exporting for Italian manufacturing firms when the dummy Di0 is replaced
by export intensity in the initial year. Fafchamps et al. (2002) use the number of years since the firm
began exporting to identify learning-by-exporting for a cross-section of Moroccan exporters.5
As mentioned in Section 1, we believe that the phenomenon of learning-by-exporting is
related, like learning-by-doing, to the intensity of exposure to new challenging tasks. Therefore, an
important aspect of our methodology is to capture learning-by-exporting using measures of export
experience that convey not only whether or not the firm has participated in export markets in the
4
Van Biesebroeck (2004) proposes a third method of estimation of Equation (2) based on Olley and Pakes (1996).
5
Blalock and Gertler (2004) introduce export intensity and the number of years a plant has exported as robustness
checks to their main regression. Unfortunately, the main variable in those regressions is a dummy for contemporaneous
exports, whose interpretation as learning-by-exporting is problematic due to the endogeneity of the export participation
decision.
10
past but also the intensity and persistence over time of the firm's exposure to export markets. In the
next section we explain in detail our approach to measuring learning-by-exporting effects.
3. Empirical Specification
The measure of plant performance used in this paper to assess the presence of learning-by-
exporting effects is total factor productivity (TFP). In the literature reviewed in Section 2,
researchers often rely on a two-step approach, first regressing output on inputs to obtain plant-level
time series of TFP and then estimating a variant of Equations (1) or (2) above with TFP as the
dependent variable. An alternative method is to test directly for learning-by-exporting effects in the
estimation of the production function, the so-called one-step approach (Van Biesebroeck, 2004;
Bigsten et al., 2004). In this paper, we show results using both approaches, though we emphasize
the two-step approach due to its greater flexibility.
In our measurement of TFP, we take into account two elements: (i) factors of production
differ in their quality, and (ii) the choice of variable inputs may be correlated with productivity
shocks unobserved by the econometrician. Accounting for differences in factor quality is important
in light of the criticism by Katayama et al. (2003) to plant-level TFP estimates that they consider to
be unreliable since physical volumes of output and inputs are not observed but rather estimated by
deflating nominal sales revenues and input expenditures using sector-wide price indexes. If there is
a positive association between factor quality and sales revenue (resulting from either a higher
volume or a better quality of output), omitting factor quality measures in the production function
will make plants using better inputs look as if they are more productive. Similarly, if plant
11
managers choose variable inputs based on knowledge of their plant's current productivity, the
estimated coefficients on variable inputs in the production function will be upwardly biased. This
bias will make plants that use relatively more variable inputs appear less productive.
In this paper, we consider the following production function:
Yit = AitLit Mit Kit exp q'Qit
l m k ( ), (3)
where Ait is total factor productivity; Lit, Mit, and Kit are, respectively, labor, intermediate inputs,
and capital; and Qit is a vector of factor quality measures. The vector Qit includes two measures of
labor quality, skill intensity Sit and wage premium Wit, and one measure of capital quality, capital
vintage Vit. Finally, we model total factor productivity as
Ait = exp(YEYEit + EEEEit +it +it ), (4)
where YEit is a measure of output experience, EEit is a measure of export experience, it a plant-
specific productivity shock known to the plant manager, and it a zero-mean productivity shock
realized after variable inputs are chosen. In our estimation, YEit is a vector including two measures
of output experience.
In the two-step approach, we estimate the production function without taking into account
the potential dependence of TFP on output and export experience:
yit = 0 + llit + mmit + kkit + SSit + WWit + VVit +it +it . (5)
As mentioned above, a major econometric problem with Equation (5) is the possibility of an
upward bias in the estimated coefficients on variable inputs (labor, intermediate inputs, skill ratio,
and wage premium) and a corresponding downward bias in the estimated coefficients on quasi-
12
fixed inputs (capital and vintage). To obtain consistent estimates of the production function
parameters, we use a modified version of the combination of parametric and nonparametric
techniques proposed by Levinsohn and Petrin (2003) [henceforth LP].
The LP estimation procedure makes use of plant-level intermediate inputs' choices to
correct for the simultaneity between variable inputs and productivity. Estimation proceeds in two
stages. First, the coefficients on labor, skill intensity, and wage premium are obtained by semi-
parametric techniques. Following LP, we assume that a plant's demand for intermediate inputs
increases monotonically with its productivity, conditional on its capital and vintage. Then, the
inverse of the intermediate inputs demand function depends only on observable intermediate inputs,
capital and vintage and its nonparametric estimate can be used to control for unobservable
productivity, removing the simultaneity bias. Second, intermediate inputs, capital and vintage
coefficients are obtained by generalized method of moments (GMM) techniques. The identification
assumption is that capital and vintage adjust with a lag to productivity.6 Further estimation details
and results for a set of industries are provided in Appendix A.
Equation (5) is estimated separately for each of twenty four 3-digit ISIC Colombian
manufacturing industries. We construct our measures of plant TFP as a^ it +it = yit -
it ^
(^ ) afterobtaining consistent production function
0 + ^llit + ^mmit + ^kkit + ^SSit + ^WWit + ^VVit
parameters. In the second step, we estimate
a^it = 0 + YEYEit + EEEEit + uit (6)
6
More specifically, we assume that productivity follows a Markov process: = E[ / ]+
it it it-1 where
it
13
by different methods, such as OLS, fixed effects, and Blundell and Bond (1998) system-GMM.
In the one-step approach, we include output and export experience directly in the production
function:
yit = 0 + llit + mmit + kkit + SSit + WWit + VVit + YEYEit + EEEEit +it +it . (7)
As above, we use intermediate inputs to correct for the endogeneity of input choices with respect to
productivity. We assume that the plant manager observes its current productivity it before making
profit-maximizing choices of labor, labor quality, and intermediates to be combined with the quasi-
fixed input capital and its quality and produce output. To obtain the coefficients on production and
export experience variables, we modify the LP estimation procedure. The main identifying
assumption is that production and export experience are taken by plant managers as state variables
like capital; hence their coefficients are obtained in the same stage of the estimation as that of
capital. All details on the estimation procedure are provided in Appendix A.
4. Data
The dataset used in this study is constructed from the 1981-1991 annual census of
Colombian manufacturing plants conducted by Departamento Administrativo Nacional de
Estadística (DANE). The census covers all manufacturing plants with ten or more employees.7 The
variables provided by the census are in current pesos, except for the number of workers and the
represents the unexpected part of current productivity to which capital and vintage do not adjust.
it
7
More specifically, DANE requires a plant to have more than ten employees to enter the census for the first time, but
then continues to cover the plant regardless of its employment levels. As a result, plants with less than ten employees are
included in the sample in almost all years.
14
consumption of electric energy. Therefore, we use a series of price indexes to convert all the
nominal variables into 1986 constant pesos. We obtain implicit price indexes for different types of
capital goods and producer price indexes (PPI) at 3-digit ISIC (revision 2) from DANE, and
construct our own indexes for domestic and imported raw materials and for exports. Details on the
construction of price indexes and other data issues are provided in Appendix B.
The main variables for our analysis are output, labor, intermediate inputs, skill intensity,
wage premium, capital, vintage, production experience, and export experience. Output Yit is
obtained as the sum of the value of domestic sales plus net inventory accumulation deflated by PPI
and the value of exports deflated by the exports price index. Labor Lit is the total number of
workers. Intermediate inputs Mit is the sum of raw materials consumption and energy consumption
in constant pesos. Raw materials consumption in constant pesos is the sum of the values of
domestic and imported raw materials consumed during the year deflated by the price indexes of,
respectively, domestic and imported raw materials. Energy in constant pesos is the sum of electric
energy consumed during the year valued at 1986 prices plus consumption of fuels and lubricants
deflated by the PPI of the petroleum refineries sector.
Our measures of labor quality are skill intensity Sit, defined as the ratio of the number of
white collar workers, managers, and technicians to the total number of workers, and the wage
premium Wit, defined as the ratio of the plant's average wage in a given year to the average wage
paid that year in the region where the plant is located.8 Bahk and Gort (1993) use the plant's
average wage as a measure of labor quality on the grounds that variations in wages "mainly
8
We consider thirteen regions: eight major metropolitan areas (Bogotá, Medellín, Cali, etc.), four regions in the interior,
and the rest of the country.
15
measure differences in skills rather than differences in the prices of identical classes of labor" (p.
565). Given the greater degree of geographical segmentation in Colombian labor markets, we
normalize average plant wages by the regional average wage.
Following Bahk and Gort (1993), our measure of capital is gross capital. Gross capital at
time t, Kit, is defined as cumulative purchases minus cumulative sales of capital goods up to t-1. To
obtain this measure we aggregate purchases and sales of four types of capital goods (buildings and
structures, machinery and equipment, transportation equipment, and office equipment) in constant
pesos. The omission of depreciation rates in the measurement of the capital stock is justified under
the assumption that maintenance outlays offset the adverse output effects of physical decay. Of
course, capital equipments of different vintages are affected by different degrees of obsolescence.
Given the observed continuous technological improvements in the international capital goods
industry, newer plants and plants that invest more frequently will most likely be more productive.9
For that purpose, our production function includes a measure of capital vintage Vit, whose
construction is explained in Appendix B.
We consider two measures of production experience in our analysis. Our first measure is the
number of years a plant has been in operation (age). A problem with this measure is that it assumes
that the plant accrues a similar level of experience each year, which is unrealistic since production
levels, which give rise to experience, are likely to vary from year to year. Our second measure is a
plant-specific index of cumulative production up to t-1. The index is scaled by the level of
9
The effect of embedded technological change on productivity is quantitatively significant. Jensen et al. (2001) find that
the 1992 cohort of new entrants into the U.S. manufacturing industry were, on average, more than 50% more productive
than the 1967 entrants in their year of entry, even after accounting for industry-wide factors and input differences.
16
production in the first year of operations of the plant.10 This measure takes into account Arrow's
assumption that learning will vary according to the degree of exposure to production experience. A
similar measure, cumulative production without scaling by production in the first year, has been
commonly used in the empirical learning-by-doing literature (Bahk and Gort (1993)). The absence
of scaling, however, is problematic in panel data regressions since differences in the scale of
production across plants are likely to confound the effect of experience on productivity for
individual plants. Thus, we believe that our plant-specific cumulative production index is a better
measure.
When accounting for learning-by-doing effects, the functional form is as important as the
specific measures of output experience used. As Young (1991) pointed out, empirical studies of
learning-by-doing have mostly ignored Arrow's (1962) assumption that learning-by-doing is
subject to sharply diminishing returns. The problem is that output experience measures have been
most often included as logarithmic terms, implying an unbounded effect of experience on
productivity. Taking this criticism into account, we enter our output experience measures in the
production function in reciprocal form. This functional form implies that the effect of experience on
productivity converges to zero, and we expect to find a negative and significant YEin our
regressions for evidence of learning-by-doing effects.
By analogy to the output experience variables, we define export experience alternatively as
the number of years in which the plant has exported up to t-1 or as an index of cumulative exports
up to t­1. Similarly to the cumulative output index, the plant's cumulative exports are scaled by the
10
This means that the index takes a value of one in the second year of operations of the plant.
17
level of exports in the first year the plant has exported. Unfortunately, we cannot use a reciprocal
functional form in our regressions because both indexes of plant export experience are zero for the
majority of observations in the sample.
Given our definitions of output and export experience, we need to restrict our main
estimating sample to plants born in 1981 or later, the first year when information on exports is
included in the census.11 Besides limiting our sample to plants born in or after 1981, we require
plants to have a minimum of three years of data and have positive values for the key variables
output, labor, intermediate inputs, capital, and wage premium. We exclude plants that do not report
data in some year between their first and last year in the survey and plants belonging to industries
with less than 100 plant-year observations. In addition, given that our output and export experience
measures depend on cumulative output and exports up to t-1, we exclude from the estimating
sample the first observation of each plant. Applying these criteria, we obtain a sample of 3,324
young plants and 16,706 plant-year observations. Finally, since our estimation procedures are
sensitive to outliers, we reduce further our sample to 3,091 plants and 15,457 plant-year
observations. The criteria for the elimination of outliers are described in Appendix B. To compare
the effect of export experience on productivity in young and old plants, Section 6 uses a larger
sample including both young and old plants. The latter plants appear continuously in the Colombian
manufacturing census since 1974. This sample includes 6,171 plants and 46,574 plant-year
observations.
11
A limitation of this procedure is that some of the "new" plants in 1981 could have actually been born before 1981, but
were smaller than the cutoff level of ten employees required to fill out the census form. Similarly, if a new owner
acquires a previously operating plant and registers it under a different name, it might be coded in the census as a new
plant. Since we do not have information to sort out these potential sources of error, we consider plants that appear for the
first time in the census as new plants.
18
We find that young plants are much smaller, since on average they employ one third of the
labor and produce one fifth of the output of old plants. While the use of skilled labor by young and
old plants is similar, the wages in the former are about one fourth lower than those in old plants.
Young plants invest substantially more than old plants, with an investment/output ratio about 70%
higher. Consequently, their capital is of a much newer vintage. This is perhaps the reason why
young plants' TFP is only about 7% lower than that of old plants, although their labor productivity
is 35% lower. Finally, while young plants are half as likely to participate in export markets, when
they do so, their average exports are only 20% less than those of old plants.
5. Main Results
Some initial insights on the relationship between productivity and exporting can be gained
from Figure 1 that shows levels and growth rates of plant TFP before, during, and after the year of
entry into export markets. More specifically, the figure plots in bold lines the estimated coefficients
of the dummy variables Dit in regressions of the form
Yit = + 'Zit + D
it+it , where
Dit =1
0
if plant i enters the export market at time t; Dit =1
a
(a<0) if plant i will enter the export
market a years after time t; and Dit =1
b
(b>0) if plant i has entered the export market b years before
time t.Zit contains year, industry, and region dummy variables, and Yit is alternatively the level (in
logs) and the average annual growth rates of plant i's TFP at time t over horizons of one, three, and
five years. The thinner lines show 95% confidence intervals around the parameter estimates.
Panel A of Figure 1 shows that the levels of TFP jump up at the time of entry into export
19
markets and remain higher after entry. Although the figure shows some support for the self-
selection hypothesis, i.e. that entrants are already more productive before entry, the estimates are
not significantly different from zero. Panel B shows that TFP grows about 6% in the year of entry.
Productivity growth continues to be positive and significantly different from zero up to four years
after entry. Panels C and D show clear trends of increased productivity growth over longer time
horizons, ranging from 3% to 4.5% between 3 and 8 years after entry.
While the results in Figure 1 are suggestive of learning-by-exporting, they should be taken
with caution as they are not based on a clear-cut comparison between a treatment and a control
group of plants. A better approach is based on the estimation of Equation (1) whose results are
shown in Table 2 for unmatched (Panel A) and matched samples (Panel B). In both cases the
treatment group consists of entrants into export markets, and the control group consists of plants
that do not enter export markets during the sample period. To avoid spurious comparisons, all
regressions include only one observation for each treated plant, and exclude plants that start
exporting in their first year of life. However, the unmatched and matched samples differ
dramatically in the number of observations in the control group. While the unmatched sample
includes all the observations for all the nonexporters, the matched sample includes a single
observation for each nonexporter that is matched to an entrant into export markets in the same
industry and year. To obtain the matched sample, we use propensity score matching based on a
probit regression explaining entry into export markets at time t.12 The probit includes as regressors
12
We thank Jens Arnold for sharing his STATA code for matching plants in the same year and industry. In our
matching, we ensure that two technical conditions are verified: (i) plants in the matched sample belong to the common
support defined by the lowest propensity score of a treated plant and the highest propensity score of a control plant, and
(ii) the balancing condition is verified. See Becker and Ichino (2002) and Leuven and Sianesi (2003).
20
one period lagged values of plant size (labor), wage, capital vintage, productivity, the real exchange
rate, the volume of exports in the industry and the region, and the number of exporters in the
industry and the region. The number of exporters in the industry, plant size, the real exchange rate,
and capital vintage are positively and significantly associated with entry into export markets.
Column (1) of Table 2 (Panel A) shows estimates of the coefficient 1 in Equation (1) with
average annual growth rates of plant TFP over one to five years horizons as dependent variables.
The regressions using the unmatched samples include initial wage, skill, size (labor), capital
intensity, and year, industry, and region dummies as controls. The regressions using the matched
samples are estimated without additional controls. Due to the small sample size, it is not possible to
include a full set of dummy variables as controls. Also, in regressions using only initial wage, skill,
size capital intensity as controls, these variables turned out to be statistically insignificant and their
inclusion did not change the estimates of the parameter of interest.13 Interestingly, the estimation
results are similar, regardless of the sample used. The average annual rate of growth of TFP of
entrants into export markets is around 3% higher than that of nonexporters in the unmatched sample
and around 4% higher in the matched sample. The regression results presented in column (2) of
Table 2 (Panels A and B) provide a different perspective as they are based on differences in the
plant's percentile in the TFP distribution for its industry and year. They suggest that between four
and five years after entry, entrants into export markets advance 12 to 14 percentiles in their
industry's TFP distribution.14
13
Incidentally, the lack of significance of the controls suggests that the matching method is accurate in identifying
nonexporters with very similar characteristics to those of the entrants into export markets.
14
Note that although the sample size is substantially smaller, the standard errors in the regressions with the matched
samples are only about 50% higher than those in the regressions with the unmatched samples.
21
The results in Table 2 are also suggestive of the presence of learning-by-exporting effects.
However, as argued in Section 1, a dummy to identify entrants into export markets does not
accurately capture their exposure to export activities. In Table 3, we present estimates of Equation
(6) using both OLS with industry dummies and fixed plant effects. The fixed effects or within
estimates are obtained by subtracting from each variable xit its plant-specific mean over time xi.
before estimation by OLS.15 All regressions include year dummies. Since conventional F-tests
reject the null hypothesis of no fixed effects, we focus on the results with fixed effects, although we
also present OLS results for comparison. Interestingly, in all cases age has an unexpected sign,
indicating that productivity decreases as plants get older. In contrast, the cumulative output index
has always the expected positive sign. These results suggest that it is the intensity of exposure to
production activities, and not the mere passage of time, that contributes to learning-by-doing.16 Our
measures of export experience are positive and significant in all regressions in Table 3. In the
fixed effects regressions, plant TFP increases 4.8% for each additional year of export experience
and 2.1% for an increase of one standard deviation (about 10) in the cumulative exports index.
Two counterarguments can be made to the proposition that export experience increases
productivity. A first counterargument is that although plants typically experience a boost in
measured TFP during the years when they export, this boost may not reflect a true productivity
increase but merely a higher utilization of existing factors in response to the increased demand
15
We also estimated random effects specifications but found that often the Hausman test rejected the exogeneity of the
regressors with respect to the random plant effects, making the fixed effects specification more appropriate.
16
Olley and Pakes (1996) also find that age is inversely associated with plant productivity. Levhari and Sheshinski
(1973) find that average workers' age is insignificant when average workers' experience is included in the production
function.
22
facing the plant. To examine this possibility, we include in columns (3), (4), (8) and (9) of Table
3 a current exports dummy. As expected, we find that while plants increase substantially their
TFP in the years when they export (about 7% in the fixed effects regressions), the effect of export
experience remains positive and statistically significant. Moreover, in the fixed effects
regressions, the estimated coefficients of export experience are essentially unchanged, with or
without the current exports dummy.
A second counterargument is that exporters are better and more productive regardless of
how much export experience they have. One way to investigate this possibility is to include an
exporter dummy variable (equal to 1 for plants that export in at least one year) in the regressions.
The results in columns (5) and (10) of Table 3 show that the export experience variables remain
positive and statistically significant after accounting for the fact that exporters are on average
more productive than nonexporters. Another way to address this point is by reestimating the
regressions in columns (1)-(4) and (6)-(9) of Table 3 for the subsample of plants that export at
least once during the sample period (2,576 observations). The results, available from the authors
upon request, are very similar to those in Table 3. The coefficients on the numbers of years
exported decrease slightly (to 4.3% and 3.5%, respectively, in the fixed effects regressions with
or without the current exports dummy), but remain highly significant.
A potential concern with the results in Table 3 is that our TFP measures may be serially
correlated. In fact, the main identifying assumption in the LP methodology used for the
estimation of the production function is that productivity follows a Markov process, which plant
managers can forecast before choosing their variable inputs. One way to address this possibility
23
is by allowing the error term in Equation (6) to be autoregressive. Given the significance of plant
effects found in Table 3, we include a fixed effect fito account for unobserved plant
heterogeneity in TFP:
a^it = 0 + YEYEit + EEEEit + fi + uit
uit = uit +it ,
-1 1, it ~ i.i.d.(0, ). (6')
This specification leads to the following estimating equation:
a^it = a^it + (1- )0 + YEYEit - YEYEit + EEEEit - EEEEit + (1- ) fi +it
-1 -1 -1 . (8)
As is well known in the econometric literature (Nickell, 1981), fixed effects estimates of this model
are biased when <1and 0. Therefore, we estimate Equation (8) using the system-GMM
method proposed by Blundell and Bond (1998). Note, however, that Equation (8) can be estimated
consistently in first differences by OLS if =1:
a^it = YEYEit + EEEEit +it (9)
In Table 4, we show the results from estimating Equation (8). As in Blundell and Bond
(1998), we estimate the equation imposing no restrictions on the coefficients on the lagged
explanatory variables. We assume that output and export experience are predetermined variables,
implying that lagged values of those variables and of the dependent variable dated t-2 and earlier
are valid instruments to estimate Equation (8) in first differences. We find, however, that including
instruments dated t-2 leads to a rejection of the Sargan test of overidentifying restrictions. Thus, in
our final specification we include as instruments lags of output and export experience variables and
of the endogenous variable dated t­3 or earlier. We find evidence of second order serial correlation
24
in the first differenced residuals of Equation (8) in estimations for the full sample.17 After extensive
experimentation with alternative instruments sets and subsamples, we find evidence of no second
order serial correlation only when we estimate Equation (8) for a subsample of young plants with 8
or 9 annual observations. The results presented in Table 4 are based on that subsample. The output
experience coefficients change their sign, though age becomes statistically insignificant. Export
experience continues to be positively associated with TFP, with each additional year of export
experience increasing TFP by about 9% while the coefficient on the cumulative exports index is
positive but not statistically significant.18 The most noticeable result in Table 4, however, is that the
estimate of is very close to 1. Using a conventional t test of H0: =1 against H1: <1, we fail
to reject the null hypothesis in both regressions with p-values of 0.25 and 0.47.
To obtain more information on the time series properties of the variables used in this model,
we test for the null hypothesis of unit roots by estimating simple AR(1) specifications by OLS.19
Our tests cannot reject the null hypothesis of a unit root in our TFP series, with p-values of 0.13
(with year dummies) and 0.46 (without year dummies). The tests overwhelmingly reject the null
hypothesis of unit roots in the output and export experience variables. Although unit root tests have
low power to distinguish between a random walk and a highly persistent AR(1) process, the
evidence suggests that assuming that =1 is a reasonable approximation. While estimating
17
By construction, the first differenced residuals of Equation (8) follow an MA(1) process; therefore, if tis i.i.d. we
should find evidence of first order but not of second or higher order correlation in these residuals. The m1 and m2
statistics reported in Table 4 test, respectively, for first and second order serial correlation in the residuals.
18
For comparison purposes we also estimate for this subpanel the regressions corresponding to Table 3 and find an
effect of export experience on TFP that is larger than for the full sample: e.g., the coefficient on the number of years
exported in the fixed effects specifications is 5.7%.
19
Bond et al. (2002) show that the t-test on the OLS coefficient of the lagged value of the series has high power when
the variance of unobserved heterogeneity is relatively small.
25
Equation (9) by OLS is perfectly feasible under this assumption, we prefer to estimate the model
using a cross-section of long-differences, defined as the difference between the first and last
observation of each plant in the sample.20 This model allow us to focus on the cross-sectional
differences in experience and productivity, exploiting the additional variability due to differences in
the number of years that plants are in the sample.21 The results are presented in Table 5 for the full
sample and for a subsample of plants that export since their first year in the sample. The results for
the full sample, shown in columns (1)-(4), are very similar to those from the fixed effects
regressions in Table 3. This is reassuring because under the assumption that =1, estimates
obtained using the within transformation are consistent. The results suggest that an additional year
of export experience increases productivity by 4.2% after accounting for current exports.
In columns (5)-(8) of Table 5 we show the results for the subsample of born exporters. It is
important to focus on this group for two reasons. First, as Hallward-Driemeier et al. (2003) point
out, focusing on plants that start exporting from their first year eliminates the problem of self-
selection of more productive plants into export markets, allowing us to identify a truly causal
effect of export experience on plant productivity. Second, when the estimating sample includes
observations for which the export experience variables are zero, it is unclear whether the
coefficients on those variables are just capturing a one-time boost in productivity when export
experience increases from zero to one.22 By including in the regression only plants with strictly
20
Recall that since our output and export experience measures depend on cumulative output and exports up to t-1, we
exclude from the estimating sample the first observation of each plant.
21
Note that the error term of the long differences between the first (2) and last (Ti ) time the plant is in the sample is
Ti
i , which is, by construction, heteroskedastic.
=3
22
We thank Eduardo Engel for pointing out this possibility.
26
positive export experience, we ensure that the estimated coefficient captures the effect of the
accumulation of additional export experience after entry into export markets on plant
productivity. The results for born exporters show that plant TFP increases 7.7% for each
additional year of export experience, after accounting for current exports. Since in this subsample
the cumulative exports index is always strictly positive, we estimate the regressions with the
cumulative exports index expressed in logs, which allows for an easier interpretation. The results
suggest that a doubling of the index increases TFP by 7% after accounting for current exports.
6. Extensions
In order to better understand why export experience is conducive to plant learning, we
consider in this section several extensions to our main results. A first question is whether only
young plants learn from the exposure to export markets. As mentioned above, we focus in this
paper on young plants because we observe their full history and measure export experience most
accurately. The inclusion of old plants in the analysis requires some assumptions. We assume
that old plants showing at least three years with zero exports before exporting for the first time
during the 1981-1991 period (for which information on exports is available) are new entrants into
export markets. Of course, it is possible that some of these plants have actually exported before
1981, but this criterion eliminates at least the group of established exporters that are likely to
export every year.
Table 6 shows estimation results for a variant of Equation (1) using a matched sample. The
regressions include two dummy variables identifying young and old entrants into export markets.
27
These specifications allow us to determine if young plants experience better performance after
entering into export markets than old plants. As for Table 2, we find that control variables are
insignificant and do not alter the estimated coefficients of interest; thus Table 6 shows results from
regressions that do not include controls. Columns (1) and (2) show the estimated coefficients on the
dummies for young and old entrants into export markets in regressions with the average annual
growth rate of TFP as dependent variable. Note that young plants entering into export markets
experience average annual rates of TFP growth around 3.5% faster than nonexporters over horizons
of two to five years after entry, while old entrants' grow over the same horizons around 1.8% faster
than nonexporters. Columns (3) and (4) also show differences in the changes of the plants' relative
position in their industry-year TFP distribution. Young plants entering into export markets move up
10 percentile points five years after entry compared to nonexporters, twice as much as old plants.
Table 7 shows the results from estimating Equations (6) and (9) with an interaction term to
capture differences in the impact of export experience on TFP for young and old plants. As in Table
5, we estimate Equation (6) with fixed effects and Equation (9) as a cross section of long
differences. We present results for the full sample in columns (1)-(4) and for the subsample of
exporters in columns (5)-(8). We include the current exports dummy in all regressions. The results
indicate that young plants learn significantly more from exporting than old plants. On average the
effect of an additional year of export experience on TFP is 6.3% for young plants and 1.9% for old
plants, and the coefficient on the cumulative exports index is 7 times higher for young plants
compared to old plants.
Another important question to investigate is whether results change when output experience
28
and export experience are included directly in the production function as in Equation (7), the so-
called one-step approach. Table 8 presents results for the five Colombian manufacturing industries
with the largest number of young plants.23 For simplicity, we show only the estimated coefficients
on output and export experience variables. Export experience is measured either as the number of
years the plant has exported or as the plant's cumulative exports index. We study whether the
inclusion of a current exports dummy or an exporter dummy affects the estimated export experience
effect. The results confirm the findings from our two-step regressions. Plant productivity decreases
with age but increases with cumulative output experience and the coefficients on age and the
cumulative output index tend to be statistically significant. As in Section 5, it appears as if the
intensity of exposure to production activities, which is a better measure of experience than the
number of years a plant has produced, leads to learning-by-doing.
The number of years the plant exported has a positive effect on TFP that is statistically
significant in all but one of the LP regressions. The sign and significance of this effect is robust to
the inclusion of either the current exports dummy or the exporter dummy in the production function
equation. On average, an additional year of exports increases plant TFP by 5.4% without controls,
by 4.7% when controlling for the exporter dummy, and by 3.4% when controlling for the current
exports dummy. These estimates are on average consistent with those obtained using the two-step
approach, but there are differences across industries: the effect is larger in the food processing and
clothing industries and smaller in the plastics and metal products industries. The cumulative exports
index is positive and statistically significant in most of the LP regressions, and three of the five
23
These industries are the same as those shown in Table A1 of Appendix A.
29
cases in which it is not significant occur for the metal products industry. Including the current
exports dummy does not alter the positive sign or the significance of the coefficient on the
cumulative exports index. This coefficient is less robust, though, to the inclusion of the exporter
dummy: in the food products and plastics industries, it remains positive but not statistically
significant. These industry-specific results suggest that the relationship between export experience
and productivity might vary across industries. Our last question is whether we can explain this
variation.
We explore two hypotheses to explain differences in learning-by-exporting across
industries. The first hypothesis is that plants have more scope for learning-by-exporting when they
export to high-income countries. This hypothesis is motivated by the presumption that consumers in
high-income countries are more discriminating about the quality of the goods they import.
Therefore, their markets are likely to be more competitive than the markets of low-income
countries. As a result, Colombian manufacturers exporting to those countries will face higher
demands on such aspects as product quality, delivery time, and post-sale services, which in turn
give managers and workers more opportunities for learning and productivity enhancement.
To investigate this possibility, we construct an additional variable using data from the
World Trade Flows, 1980-1997 database (WTDB) compiled by R. Feenstra: the share of industry
exports going to high-income countries.24 The list of high-income countries is obtained from the
World Bank.25 To match these additional data to our main dataset, we convert the industry
classification codes of the WTDB files from the U.S. Department of Commerce Bureau of
24
The data can be downloaded from http://data.econ.ucdavis.edu/international/.
30
Economic Analysis's industry classification into the ISIC rev. 2 by aggregating the twenty four
industries in our sample into twenty industries before conducting the regression analysis. It should
be noted that during the sample period a rather large share of Colombian manufacturing exports
went to Panama and the Netherlands Antilles. Since these are important ports for transshipments,
we assume that the share of Colombian exports to these countries that are transshipped to high-
income countries in each industry can be approximated by the share of exports of Panama and the
Netherlands Antilles in that industry that go to high-income countries.
In Table 9 we show regression results from estimating a modified version of Equation (6) to
which we add the interaction between an export experience variable the share of industry exports
going to high-income countries. We estimate all the regressions in Table 9 by fixed plant effects,
and include the current exports dummy. The results in columns (1) and (2) show that the interaction
between any of the export experience variables and the share of industry exports going to high-
income countries is always positive and significant. To gain a better perspective on the economic
significance of these estimates, we compare the textile and clothing industries, which direct on
average 70% of their exports to high-income countries during the sample period, with the metal
products industry (ISIC 381), whose share of exports going to high-income countries is only 24%.
A simple calculation based on the regression results shows that an additional year of export
experience increases TFP by 5.6% in the textile and clothing industries compared to 2.7% in the
metal products industry. Similarly, the coefficient on the cumulative exports index is 2.5 higher in
the textile and clothing industry than in the metals products industry.
25
http://www.worldbank.org/data/countryclass/countryclass.html.
31
The second hypothesis is that learning-by-exporting is positively associated with the total
value of exports of the industry. This hypothesis is motivated by the possibility that network
externalities facilitate the access to export markets. A higher value of exports from a particular
Colombian industry may suggest that such industry has more developed channels of distribution,
making it easier for newcomers to export markets to start exporting. If correct, this perspective
implies that in industries characterized by low value of exports the barriers of access to export
markets are substantial. In those industries, we should not find much evidence of learning-by-
exporting due to the difficulties faced by plants in trying to establish themselves as exporters.
In columns (3) and (4) of Table 9 we show regression results from estimating a modified
version of Equation (6) to which we add the interaction between an export experience variable and
the log of the value of industry exports.26 The results provide support to this hypothesis as well. In
all cases the coefficients on the interaction between one of the export experience variables and the
value of industry exports are positive and significant, while the coefficients on the experience
variables per se are negative. To interpret the results, consider again the differences between the
textile and clothing industries, with average annual exports of $327 millions during the sample
period, with the metal products industry, which exports only $29 millions per year. Using the
estimates in columns (3) and (4), we find that an additional year of export experience increases TFP
by 6.3% in the textile and clothing industries compared to 2.5% in the metal products industry.
Similarly, the coefficient on the cumulative exports index is 3.4 times higher in the textile and
clothing industry than in the metals products industry.
26
Industry exports are measured in thousands of current U.S. dollars.
32
7. Conclusion
While the hypothesis that firms improve their productivity when exposed to competitive
export markets--learning-by-exporting--is intuitively appealing, the corresponding empirical
evidence has been inconclusive. Researchers have often favored the alternative hypothesis that
firms that improve their productivity self-select into export markets. In this paper we consider the
parallels between learning-by-exporting and learning-by-doing. From Arrow's (1962) classical
study of learning-by-doing, we know that learning occurs when workers and managers gain
experience in solving new technical and organizational problems, and that learning associated with
repetitive tasks is subject to sharply diminishing returns. Arrow's characterization of learning-by-
doing applies to learning-by-exporting because firms breaking into export markets need to solve
new problems, such as adopting new technical standards, introducing more efficient equipment, and
ensuring product quality to satisfy sophisticated consumers. Drawing on this characterization, we
focus our empirical investigation of learning-by-exporting on young plants, which are much more
likely than old, established plants to face new technical and organizational challenges. We also
favor using measures of export experience to study whether productivity improvements are
associated with the extent of exposure to export markets.
We find strong evidence of learning-by-exporting for our sample of young Colombian
manufacturing plants. First, we find that young plants that enter export markets experience annual
average rates of TFP growth between 3% and 4% higher than those of young plants that never
export. This gap is robust to the use of matching methods and to the use of the plant percentile in
the industry-year distribution of TFP as an alternative measure of performance. Second, we find
33
that TFP increases between 4% and 5% for each additional year a plant has exported, after
accounting for the effect of current exports on TFP. A particularly important issue in our empirical
specification is to take into account the persistence of TFP. In our data we cannot reject the
hypothesis that TFP has a unit root. Therefore, using differences or the within transformation
produces consistent estimates of the effect of export experience on plant TFP. Third, our results on
export experience are robust to the use of different subsamples of our main dataset, such as the
subsample of plants that export in at least one year (exporters) and the subsample of plants that start
exporting from their first year (born exporters).
Fourth, using a larger dataset that includes also old, established plants, we compare the
effect of entry into export markets and export experience on TFP for young and old plants. We find
that the gap in annual average rates of TFP growth between entrants to the export markets and
nonexporters is 3.5% for young plants compared to 1.8% for old plants. We also find that each
additional year of export experience increases TFP by 6.3% in young plants, compared to 2% in old
plants. Fifth, we include export experience directly in the estimation of the production functions of
the five largest Colombian manufacturing industries, the so-called one-step approach. The results
confirm that export experience variables have a positive and generally significant effect on young
plants' TFP. The estimates of the effect of an additional year of export experience on TFP range
between 3.4% and 5.4% on average, which are consistent with those obtained using the two-step
approach. These regressions uncover important differences in the magnitude of the learning-by-
exporting effect across industries. To explain these differences, we augment the dataset with
Colombian export data by industry and country of destination. Our results, using the two-step
34
approach, suggest that young Colombian manufacturing plants learn the most from exporting if they
produce in industries that (i) deliver a larger percentage of their exports to high-income countries
and (ii) are characterized by a larger volume of exports.
As mentioned in Section 1, evidence of improvements in productivity at the
microeconomic level has supported various trade and development policies. Our robust evidence of
TFP improvements for young plants as a result of learning-by-exporting points to two general
policy recommendations. The first is to avoid policies that discourage access of domestic plants to
export markets. Since plant productivity increases with cumulated export experience, policy makers
should try to avoid policies that lead to marked drops or instability in the profitability of exporting.
The second recommendation is to foster a competitive business environment that facilitates the
reallocation of factors of production toward their most efficient uses. As young plants clearly
benefit from exporting, an institutional framework that facilitates the process of creative-destruction
by which failing plants give rise to new plants will allow the expedient redeployment of resources
and entrepreneurial talent to productivity-enhancing exporting activities.
35
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Bond, S., C. Nauges, and F. Windmeijer, "Unit Root and Identification in Autoregressive Panel
Data Models: A comparison of Alternative Tests," Mimeo, Institute of Fiscal Studies,
London.
Castellani, D. (2002), "Export Behaviour and Productivity Growth: Evidence from Italian
Manufacturing Firms," Weltwirtschaftliches Archiv 138 (4), 605-628.
Clerides, S., S. Lach, and J. Tybout (1998), "Is Learning-by-Exporting Important? Micro-Dynamic
Evidence from Colombia, Mexico, and Morocco," Quarterly Journal of Economics 113 (3),
903-47.
De Loecker, J. (2004), "Do Exports Generate Higher Productivity? Evidence from Slovenia,"
Discussion Paper No. 151/2004, LICOS Centre for Transition Economies.
Delgado, M., J. Farias, and S. Ruano (2002), "Firm Productivity and Export Markets: A Non-
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Fafchamps, M., S. El Hamine, and A. Zeufack (2002), "Learning to Export: Evidence from
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African Economies, Oxford University.
Fernandes, A. (2003), "Trade Policy, Trade Volumes and Plant-Level Productivity in Colombian
Manufacturing Industries," World Bank Working Paper No. 3064.
Girma, S., D. Greenaway, and R. Kneller (2004), "Does Exporting Increase Productivity? A
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855-866.
Hahn, C. (2004), "Exporting and Performance of Plants: Evidence from Korean Manufacturing,"
NBER Working Paper No. 10208.
Hallward-Dremeier, M. G. Iarossi, and K. Sokoloff (2002), "Exports and Manufacturing
Productivity in East Asia: A Comparative Analysis with Firm-Level Data," NBER Working
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Isgut, A. (2001), "What's Different about Exporters: Evidence from Colombian Manufacturing,"
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Jensen, J., R. McGuckin, and K. Stiroh (2001), "The Impact of Vintage and Survival on
37
Productivity: Evidence from Cohorts of U.S. Manufacturing Plants," Review of Economics
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38
Table 1: Selective Review of Learning-by-Exporting (LBE) Studies using Plant-level
Data (a)
Measurement Method and Evidence of
Study, Country, and Sample Period Estimation Technique (b) LBE?
Bernard & Wagner (1997): Germany 1978- (1) OLS No
1992
Clerides et al. (1998): Colombia 1981-1991, (2) FIML including an equation No
Morocco 1984-1991 for export participation
(2) GMM Some (Morocco)
Bernard and Jensen (1999): USA 1984-1992 (1) OLS No
Kraay (1999): China 1988-1992 (2) Instrumental variables (lagged Yes
export intensity)
Aw et al. (2000): Taiwan 1981, 1986, and (1) OLS Some (Taiwan)
1986; Korea 1983, 1988, and 1993
Isgut (2001): Colombia 1981-1991 (1) OLS No
Delgado et al. (2002): Spain 1991-1996 (1) Nonparametric estimation Some (young
plants)
Castellani (2002): Italy 1989-1994 (1) OLS No
(1) OLS (export intensity in t=0) Yes
Hallward-Driemeier et al. (2002): Indonesia, (1) OLS, cross section (dummy Yes (except
Korea, Malaysia, Phillipines, Thailand 1999 for born exporters) Korea)
Fafchamps (2002): Morocco 1999 (1) Instrumental variables, cross- No
section (years since first export)
Baldwin & Gu (2003): Canada 1974, 1979, (1) OLS Yes
1984, 1990, and 1996 (2) SYS-GMM Yes
Van Biesebroeck (2004): Cameroon, Kenya, (2) SYS-GMM Yes
Tanzania, Zambia, Zimbabwe 1992-1994; (2) FIML as in Clerides et al. Yes
Ghana 1991-1993; Cote d'Ivoire 1994-1995 (2) OP Yes
Girma et al. (2004): UK 1988-1999 (1) Matched samples Yes
Bigsten et al. (2004): Cameroon, Kenya, (2) FIML as in Clerides et al. No
Ghana, and Zimbabwe 1992-1995 (2) FIML nonparametric errors Yes
Hahn (2004): Korea 1990-1998 (1) OLS Yes
Blalock and Gertler (2004): Indonesia 1990- (2) Various (contemporaneous Yes
1996 exports)
Arnold and Hussinger (2004): Germany 1992- (1) Matched samples No
2000
De Loecker (2004): Slovenia 1994-2000 (1) Matched samples Yes
Alvarez and Lopez (2004): Chile 1990-1996 (1) OLS No
Notes: (a) The information included in this table is based on what we consider to be the main
regression(s) used to measure learning-by-exporting effects in each of the papers cited. (b) Most
studies use dummy variables to define the treatment group of exporters or to indicate whether the
firm has exported in a previous period; other types of export variables are noted in parentheses.
When a study uses more than one method, we enter them in separate rows in the same cell.
39
Figure 1: Plant TFP and TFP Growth Before and After Entry into Export Markets
0.3 0.14
rate
0.2 th 0.07
worg
TFP 0.1 alun 0
ogl an
e
0 -0.07
eragv
A
-0.1 -0.14
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years to or after entry Years to or after entry
A. TFP Levels B. TFP Growth over One-year Horizons
0.1 0.1
rate etar
th h
worg 0.05 owt 0.05
alun grl
an nnuaa
e 0 egar 0
eragv
A Ave
-0.05 -0.05
-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years to or after entry Years to or after entry
C. TFP Growth over Three-year Horizons D. TFP Growth over Five-year Horizons
40
Table 2: Average Annual Growth Rate of Plant TFP and Plant TFP Percentile
Changes after Entry into Export Markets
A. Unmatched Sample
Time Number Number TFP TFP
Horizon of of Growth Percentile
Entrants Nonexporters
(1) (2)
1 Year 231 12881 0.015 1.3
(0.014) (1.5)
2 Years 154 10255 0.030 *** 5.5 ***
(0.010) (2.0)
3 Years 106 7629 0.026 *** 5.1 **
(0.008) (2.6)
4 Years 76 5604 0.037 *** 14.0 ***
(0.008) (3.2)
5 Years 56 3953 0.030 *** 12.8 ***
(0.007) (3.8)
B. Matched Sample
Time Number Number TFP TFP
Horizon of of Growth Percentile
Entrants Nonexporters
(1) (2)
1 Year 228 187 0.036 * 1.8
(0.021) (2.2)
2 Years 151 126 0.058 *** 10.0 ***
(0.016) (3.3)
3 Years 103 94 0.041 *** 9.2 **
(0.014) (4.0)
4 Years 73 66 0.043 *** 14.3 ***
(0.011) (4.7)
5 Years 53 48 0.033 *** 11.8 **
(0.010) (4.6)
Notes: Standard errors in parentheses. ***, ** and * indicate significance at the
1%, 5% and 10% confidence levels, respectively. TFP percentile indicates the
percentile in the TFP distribution for the plant's industry in a given year. In
Panel B, the sample used in the regressions is a matched sample where each
entrant into export markets is matched to a control plant in the same industry
and year.
41
Table 3. The Effect of Learning-by-Doing and Learning-by-Exporting on Plant Productivity
OLS Fixed OLS Fixed OLS OLS Fixed OLS Fixed OLS
Effects Effects Effects Effects
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Number of Years Plant Exported 0.039*** 0.048*** 0.022*** 0.047*** 0.028***
(0.005) (0.005) (0.006) (0.005) (0.006)
Cumulative Exports Index 0.0029*** 0.0020*** 0.0024*** 0.0020*** 0.0026***
(0.0007) (0.0004) (0.0007) (0.0004) (0.0007)
Current Exports Dummy 0.085*** 0.066*** 0.104*** 0.070***
(0.015) (0.011) (0.012) (0.011)
Exporter Dummy 0.045*** 0.066***
(0.010) (0.008)
Inverse of Plant Age 0.351*** 0.123*** 0.330*** 0.116*** 0.335*** 0.350*** 0.140*** 0.320*** 0.132*** 0.324***
(0.035) (0.045) (0.035) (0.045) (0.035) (0.035) (0.044) (0.035) (0.044) (0.035)
Inverse of Cumulative Output Index -0.282*** -0.097** -0.265*** -0.091** -0.268*** -0.288*** -0.115*** -0.259*** -0.108*** -0.260***
(0.032) (0.040) (0.032) (0.040) (0.032) (0.032) (0.040) (0.032) (0.040) (0.032)
Industry Effects (3-digit) Yes Yes Yes Yes Yes Yes
42 N. Observations 15457 15457 15457 15457 15457 15457 15457 15457 15457 15457
R-squared 0.95 0.99 0.95 0.99 0.95 0.95 0.99 0.95 0.99 0.95
Notes: Robust standard errors in parentheses. ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels, respectively. All regressions include year
dummies. The Current Exports Dummy equals 1 for plant i in year t if plant i engages in exports in year t . The Exporter Dummy equals 1 for plant i in all years if plant
i engages in exports in at least one sample year.
Table 4. Accounting for Plant Productivity Dynamics
System-GMM System-GMM
(1) (2)
Lagged Productivity (t-1) 0.995 *** 0.999 ***
(0.008) (0.006)
Number of Years Plant Exported (t) 0.092 **
(0.037)
Lagged Number of Years Plant Exported (t-1) -0.097 **
(0.043)
Cumulative Export Index (t ) 0.0013
(0.0018)
Lagged Cumulative Export Index (t -1) -0.0010
(0.0024)
Inverse of Plant Age (t) -1.213 -1.528
(1.893) (1.852)
Lagged Inverse of Plant Age (t-1) 1.185 1.474
(1.292) (1.265)
Inverse of Cum. Output Index (t) 1.578 * 1.925 **
(0.912) (0.878)
Lagged Inverse of Cum. Output Index (t-1) -1.287 * -1.588 **
(0.776) (0.751)
N. Observations 3038 3038
Tests of GMM Consistency (P-values)
Sargan 0.270 0.262
m1 0 0
m2 0.215 0.208
Notes: The dependent variable is current productivity. All regressions include year dummies.
Robust standard errors in parentheses. ***, ** and * indicate significance at the 1%, 5% and
10% confidence levels, respectively. The sample consists of plants remaining in the sample
for 8 years or longer. The Current Exports Dummy equals 1 for plant i in year t if plant
i engages in exports in year t . Lags dated t-3 and earlier of the output experience variables,
the export experience variable and the dependent variable are used as instruments in the first
difference equation. The first difference dated t-2 of the output experience variables, the
export experience variable and the dependent variable are used as instruments in the levels
equation. m1 is a test for first order serial correlation in the residuals of the first-differenced
equation and m2 is a test for second order serial correlation in the residuals of the first-
differenced equation.
43
Table 5. The Effect of Learning-by-Doing and Learning-by-Exporting on Plant Productivity Using Cross-Sections of Long Differences
Full Sample Subsample of Born Exporters
(1) (2) (3) (4) (5) (6) (7) (8)
Number of Years Plant Exported 0.046*** 0.042*** 0.078*** 0.077***
(0.005) (0.005) (0.018) (0.018)
Cumulative Exports Index 0.0017*** 0.0017*** 0.071** 0.070**
(0.0003) (0.0003) (0.035) (0.035)
Current Exports Dummy 0.093*** 0.111*** 0.104** 0.108**
(0.019) (0.019) (0.049) (0.050)
Inverse of Plant Age 0.319*** 0.308*** 0.336*** 0.321*** 0.714* 0.531 0.076 -0.103
(0.071) (0.071) (0.072) (0.071) (0.388) (0.385) (0.392) (0.398)
Inverse of Cum. Output Index -0.167*** -0.148** -0.206*** -0.179*** -0.311 -0.171 -0.212 -0.072
(0.065) (0.065) (0.065) (0.065) (0.375) (0.370) (0.426) (0.433)
N.Observations 3091 3091 3091 3091 130 130 130 130
Adjusted R-squared 0.04 0.04 0.02 0.03 0.36 0.37 0.26 0.28
44
Notes: The dependent variable is the change in productivity between the last and the first year of the plant in the sample. The symbol
represents, for any regressor, the change in that regressor between the last and the first year of the plant in the sample. All regressions include
year dummies. Robust standard errors in parentheses. ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels respectively.
The Current Exports Dummy equals 1 for plant i in year t if plant i exports in year t . In columns (5)-(8) the cumulative exports index is
expressed in logs.
Table 6. Average Annual Growth Rate of Plant TFP and Plant TFP Percentile Changes after Entry into Export Markets for
Young and Old Plants
Time Number of Number of Number TFP TFP
Horizon Young Old of Growth Percentile
Entrants Entrants Nonexporters
Young Old Young Old
Plants Plants Plants Plants
(1) (2) (3) (4)
1 Year 216 301 405 0.028 0.025 3.0 1.4
(0.017) (0.015) (1.9) (1.4)
2 Years 151 244 310 0.037 *** 0.010 10.6 *** 1.5
(0.012) (0.010) (2.6) (1.7)
3 Years 104 202 244 0.031 *** 0.018 ** 13.4 *** 2.9
(0.010) (0.008) (3.6) (2.1)
4 Years 73 169 195 0.038 *** 0.019 ** 17.4 *** 2.7
(0.010) (0.008) (4.7) (2.4)
5 Years 54 135 148 0.033 *** 0.021 ** 16.0 *** 4.3
(0.009) (0.007) (6.1) (3.0)
Notes: Standard errors in parentheses. ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels,
respectively. The sample used in the regressions is a matched sample where each entrant into export markets is matched to a
control plant in the same industry and year. Columns (1) and (2) show coefficients obtained from a single regression for each
time horizon having TFP growth as dependent variable. Columns (3) and (4) show coefficients obtained from a single
regression for each time horizon having TFP percentile as dependent variable.
45
Table 7. The Effect of Learning-by-Doing and Learning-by-Exporting on Plant Productivity for Young and Old Plants
Full Sample Subsample of Exporters
Fixed Effects Long Differences Fixed Effects Long Differences
(1) (2) (3) (4) (5) (6) (7) (8)
Number of Years Plant Exported * Young 0.067*** 0.057*** 0.065*** 0.061***
(0.004) (0.007) (0.005) (0.010)
Number of Years Plant Exported * Old 0.022*** 0.018** 0.017*** 0.020**
(0.003) (0.007) (0.004) (0.009)
Cumulative Exports Index * Young 0.0027*** 0.0024*** 0.0024*** 0.0019***
(0.0004) (0.0007) (0.0004) (0.0007)
Cumulative Exports Index * Old 0.0004*** 0.0003 0.0003** 0.0004
(0.0001) (0.0003) (0.0001) (0.0003)
Current Exporter Dummy 0.076*** 0.087*** 0.115*** 0.142*** 0.069*** 0.055*** 0.127*** 0.092***
(0.006) (0.006) (0.166) (0.015) (0.007) (0.007) (0.022) (0.021)
Inverse of Plant Age 0.223*** 0.225*** 0.416*** 0.438*** 0.354*** 0.279*** 0.801*** 0.783***
46 (0.040) (0.040) (0.082) (0.082) (0.107) (0.104) (0.252) (0.252)
Inverse of Cumulative Output Index -0.194*** -0.209*** -0.405*** -0.441*** -0.311*** -0.313*** -0.755*** -0.817***
(0.037) (0.037) (0.077) (0.077) (0.097) (0.095) (0.231) (0.230)
N. Observations 40208 40208 5455 5455 6351 6351 806 806
R-squared 0.99 0.99 0.06 0.05 0.99 0.99 0.21 0.18
Notes: The dependent variable is plant productivity in columns (1), (2), (5) and (6) and the change in productivity between the last and the first year of the
plant in the sample in columns (3), (4), (7) and (8). All regressions include year dummies. Robust standard errors in parentheses. ***, ** and * indicate
significance at the 1%, 5% and 10% confidence levels, respectively. The Current Exporter Dummy equals 1 for plant i in year t if plant i engages in exports in
year t.
Table 8. Results from One-Step Estimation of the Effect of Learning-by-Doing and Learning-by-Exporting on Plant Productivity
Number of Years Exported Cumulative Exports Index
(1) (2) (3) (4) (5) (6)
311 Food Products (1937 Obs.)
Inverse of Plant Age 0.072 0.026 0.116 0.175 * 0.144 * 0.057
(0.090) (0.085) (0.095) (0.105) (0.086) (0.103)
Inverse of Cum. Output Index -0.070 -0.020 -0.097 -0.151 * -0.124 * -0.054
(0.079) (0.074) (0.082) (0.085) (0.071) (0.081)
Export Experience 0.060 ** 0.048 * 0.046 ** 0.017 * 0.020 * 0.005
(0.028) (0.026) (0.023) (0.009) (0.010) (0.007)
Current Exports Dummy 0.038 0.166 ***
(0.045) (0.047)
Exporter Dummy 0.103 0.035
(0.089) (0.100)
321 Textiles (997 Obs.)
Inverse of Plant Age 0.087 0.095 0.362 * 0.230 0.345 ** 0.193
(0.174) (0.165) (0.219) (0.165) (0.169) (0.174)
Inverse of Cum. Output Index -0.204 * -0.213 * -0.360 ** -0.195 -0.319 *** -0.183
(0.122) (0.124) (0.171) (0.130) (0.106) (0.125)
Export Experience 0.036 * 0.028 * 0.059 ** 0.009 ** 0.009 *** 0.009 ***
(0.020) (0.016) (0.026) (0.004) (0.002) (0.003)
Current Exports Dummy 0.133 *** 0.040 *
(0.023) (0.021)
Exporter Dummy -0.013 -0.009
(0.082) (0.111)
322 Apparel (3045 Obs.)
Inverse of Plant Age 0.268 *** 0.200 * 0.250 ** 0.283 ** 0.252 ** 0.250 **
(0.101) (0.107) (0.100) (0.119) (0.108) (0.105)
Inverse of Cum. Output Index -0.216 *** -0.161 * -0.206 ** -0.231 ** -0.219 ** -0.210 **
(0.083) (0.089) (0.087) (0.104) (0.089) (0.083)
Export Experience 0.092 *** 0.041 * 0.065 ** 0.018 *** 0.013 ** 0.015 **
(0.023) (0.021) (0.026) (0.007) (0.006) (0.007)
Current Exports Dummy 0.157 *** 0.159 ***
(0.027) (0.027)
Exporter Dummy 0.095 0.156 ***
(0.063) (0.056)
356 Plastics (914 Obs.)
Inverse of Plant Age 0.320 * 0.362 * 0.280 * 0.268 * 0.288 * 0.223
(0.181) (0.187) (0.160) (0.142) (0.149) (0.164)
Inverse of Cum. Output Index -0.262 * -0.279 ** -0.247 ** -0.240 ** -0.257 ** -0.233 **
(0.136) (0.131) (0.115) (0.094) (0.108) (0.110)
Export Experience 0.038 ** 0.026 * 0.034 ** 0.003 ** 0.002 * 0.002
(0.016) (0.015) (0.014) (0.002) (0.001) (0.0017)
Current Exports Dummy 0.077 * 0.068 *
(0.041) (0.037)
Exporter Dummy 0.044 0.106
(0.075) (0.089)
381 Metal Products (1218 Obs.)
Inverse of Plant Age 0.187 * 0.222 * 0.188 0.226 * 0.266 ** 0.238 *
(0.108) (0.117) (0.118) (0.118) (0.125) (0.127)
Inverse of Cum. Output Index -0.168 ** -0.195 ** -0.171 * -0.205 ** -0.232 ** -0.199 *
(0.082) (0.097) (0.098) (0.100) (0.107) (0.110)
Export Experience 0.045 ** 0.024 0.030 * 0.001 0.001 0.001
(0.023) (0.017) (0.018) (0.006) (0.004) (0.004)
Current Exports Dummy 0.114 ** 0.094 *
(0.057) (0.049)
Exporter Dummy 0.075 0.160 *
(0.079) (0.083)
Notes: All coefficients are obtained from regressions that include also additional inputs (labor, wage premium, skill intensity, materials,
capital and vintage) estimated by a modified Levinsohn-Petrin procedure. Bootstrapped standard errors in parentheses. ***, ** and *
indicate significance at the 1%, 5% and 10% confidence levels, respectively.
47
Table 9. The Effect of Export Experience on Plant Productivity Differentiated by Export Destination and Value of
Exports in the Industry
(1) (2) (3) (4)
Number of Years Plant Exported 0.012 -0.140***
(0.008) (0.031)
Cumulative Exports Index 0.0002 -0.0130**
(0.0005) (0.0056)
Number of Years Plant Exported * Share of Industry Exports to
High Income Countries 0.063***
(0.014)
Cumulative Exports Index * Share of Industry Exports to High
Income Countries 0.0034***
(0.0010)
Number of Years Plant Exported * Log of Value of Industry
Exports 0.016***
(0.003)
Cumulative Exports Index * Log of Value of Industry Exports
0.0014***
(0.0005)
Current Exports Dummy 0.068*** 0.070*** 0.681*** 0.070***
(0.011) (0.011) (0.011) (0.011)
Inverse of Plant Age 0.114*** 0.135*** 0.111** 0.131***
(0.044) (0.044) (0.045) (0.044)
Inverse of Cumulative Output Index -0.089** -0.111*** -0.089** -0.108***
(0.040) (0.040) (0.040) (0.040)
N. Observations 15457 15457 15457 15457
Adjusted R-squared 0.98 0.98 0.98 0.98
Notes: The dependent variable is plant productivity. All the regressions include year dummies and are estimated by
fixed (plant) effects. Robust standard errors in parentheses. ***, ** and * indicate significance at the 1%, 5% and
10% confidence levels, respectively. The Current Exporter Dummy equals 1 for plant i in year t if plant i engages in
exports in year t.
48
APPENDIX A: ESTIMATION METHOD
A1. Estimation of Equation (5)
We assume that in any year t the manager observes the plant's current
productivityit before choosing labor lit , labor quality Sit and Wit , and intermediates mit
to combine with the quasi-fixed inputs, capital kit and its quality Vit for the production of
output yit . Since it is known to the plant manager but unknown to the econometrician
and may be positively correlated with lit , Sit , Wit and mit , it generates a potential
simultaneity bias that is addressed by our estimation procedure. The plant's variable input
demands, derived from profit maximization, depend on privately known productivity,
capital, and capital vintage. The intermediate inputs demand function mit = m(it ,kit ,Vit )
can be inverted to obtain a productivity function by imposing the following monotonicity
assumption: conditional on capital and its vintage, the demand for intermediates increases
with productivity. Note that the productivity function it = (mit ,kit ,Vit ) depends on
observable variables only. The first stage of the estimation proceeds by rewriting
Equation (5) in a partially linear form:
yit = llit + SSit + WWit +(mit ,kit ,Vit )+it , (A1)
where
(mit,kit,Vit )= o + mmit + kkit + VVit +(mit,kit,Vit ). (A2)
We allow the functions m() . , (), and () to differ across a period of recession (1982-
. .
1985) and a period of expansion (1986-1991) in Colombia. Since E[it | mit ,kit ,Vit ]= 0 ,
taking the difference between Equation (A1) and its expectation conditional on
intermediate inputs, capital, and vintage generates the following expression:
49
yit - E[yit | mit ,kit ,Vit ]= l (lit - E[lit | mit ,kit ,Vit ])+
S(Sit - E[Sit |mit,kit,Vit ])+ W (Wit - E[Wit |mit,kit,Vit])+it (A3)
Equation (A3) is estimated by OLS (with no constant) to obtain consistent parameter
estimates for labor, skill intensity, and wage premium. The conditional expectations in
Equation (A3) are the intercepts of locally weighted least squares (LWLS) regressions of
output, labor, skill intensity, and wage premium on (mit ,kit ,Vit ) (see Fernandes (2003) for
further details). After obtaining estimates for (l ,S ,W ), we estimate the function () .
as a LWLS regression of yit - )llit - )SSit - )WWit on (mit ,kit ,Vit ).
The second stage of the estimation obtains consistent estimates for (m,k ,V ),
assuming that productivity follows a first order Markov process as in Olley and Pakes
(1996): it = E[it |it ]+it where it is the unexpected productivity shock and is
-1
independent and identically distributed (i.i.d.). The estimation strategy is based on the
identification assumption that capital and capital vintage may be correlated with expected
productivity but are uncorrelated with the unexpected productivity shock. The following
three moment conditions are obtained by taking the expectation of Equation (5)
conditional on, respectively, lagged intermediates, capital, and vintage, taking into
account the fact that it follows a first order Markov process:
E yit - ^llit - ^SSit - ^WWit - mmit - kkit - VVit - E[it |it ]| mit
[ ]
-1 -1
= E[it +it | mit ]= 0
-1 (A4)
E yit - ^llit - ^SSit - ^WWit - mmit - kkit - VVit - E[it |it ]| kit
[ ]
-1
= E[it +it | kit ]= 0 (A5)
50
E yit - ^llit - ^SSit - ^WWit - mmit - kkit - VVit - E[it |it ]|Vit
[ ]
-1
= E[it +it |Vit ]= 0 (A6)
Equations (A4)-(A6) indicate that intermediates in year t-1, and capital and vintage in
year t are uncorrelated with the unexpected productivity shock in year t. The residuals
it +it are calculated using the estimated coefficients l,^S,^W , candidate parameter
( ^ )
values ( * * * )
m ,k , V , and a nonparametric estimate for E[it | it ] obtained as a LWLS
-1
regression of (it + it )* = yit - )llit - )SSit - )WWit - mmit - kkit - kkit - VVit (from
* * * *
Equation (5)) on (it )* = )(mit ,kit,Vit )- mmit - kkit - VVit (from Equation (A2)). We
* * *
construct a generalized method of moments (GMM) criterion function which weights the
plant-year moment conditions, Equations (A4)-(A6), by their variance-covariance matrix.
Our estimation algorithm uses OLS estimates of intermediates, capital, and vintage
coefficients as initial parameter values and iterates on the sample moment conditions to
match them to their theoretical value of zero and reach final parameter estimates. We use
a derivative optimization routine complemented by a grid search. When the parameters
that minimize the criterion function are obtained from grid search, these parameters are
used as initial values for the derivative optimization routine to reach more precise final
(m,k,V ) values. The standard errors for the parameter estimates are obtained by
bootstrap. The bootstrap procedure consists of sampling randomly with replacement
plants from the industry's original sample, matching or exceeding in any year the number
of plant-year observations in that sample. If randomly selected, a plant is taken as a block
(i.e. all of its observations are included in the bootstrap sample). We obtain estimates of
51
(l,S,W ,m,k,V )for 100 bootstrap samples. The standard deviation of a parameter
across bootstrap samples constitutes its bootstrapped standard error.
We estimate Equation (5) separately for twenty-four 3-digit ISIC Colombian
manufacturing industries. Table A1 shows regression results for the five Colombian
industries with the largest number of young plants: food products (ISIC 311), textiles
(ISIC 321), apparel (ISIC 322), plastics (ISIC 356), and metal products (ISIC 381).
Columns (1) and (2) show results for production functions without factor quality
variables, and columns (3) and (4) add wage premium, skill intensity, and vintage. OLS
results are shown for comparison. Under the assumption that variable inputs' coefficients
are upward biased and quasi-fixed inputs' coefficients are downward biased, the results
suggest that the LP procedure corrects these biases for about two-thirds of the estimated
parameters. It should be noted that bootstrapped standard errors are larger than OLS
standard errors, especially for the coefficients obtained in the second stage of the LP
procedure.
A2. Estimation of Equation (7)
The first stage of the estimation is very close to that described above for Equation
(5). The main difference is that the productivity function resulting from the inversion of the
intermediate inputs demand function depends on additional state variables, the output
experience and export experience variables: it = (mit ,kit,Vit ,YEit ,EEit ). Thus, the first
stage requires the estimation of LWLS regressions of output, labor, skill intensity, wage
premium, and yit - )llit - )SSit - )WWit on (mit ,kit ,Vit ,YEit ,EEit ). Note that in some of the
variants of Equation (7) presented in the paper we use, instead of LWLS, a third degree
polynomial in it = (mit ,kit,Vit ,YEit ,EEit ) to approximate the function () . and obtain
52
consistent parameter estimates for labor, skill intensity, and wage premium, as well as to
obtain an estimate of () . . This choice is made for computational ease, as the two types of
approximation give very similar results. In the second stage of the estimation, the GMM
criterion function includes two additional moment conditions for the output and export
experience variables. The residuals used in the moment conditions subtract from output the
contribution of inputs and input quality (as in Equations (A4)-(A6)) but also the contribution
of the output and export experience variables.
To check the robustness of our results we also estimate Equation (7) including (i)
a dummy variable representing current exports (equal to 1 in any year when the plant is
exporting) and (ii) a dummy variable representing exporter status (equal to 1 in all years
for a plant if that plant exports in at least one sample year). The coefficient on the current
exports dummy is estimated in the first stage of our modified LP procedure because a
plant's decision to export, alike the usage of variable inputs, may be affected by
productivity shocks not observed by the econometrician. In contrast, the exporter dummy
is treated as a state variable thus its coefficient is estimated in the second stage of our
modified LP procedure.
APPENDIX B
B1. Price indexes
To obtain price indexes for domestic raw materials, we construct a matrix A with
typical element {aij} equal to the share of raw materials originating in industry i in the
total value of raw materials used by industry j aggregating data from Colombian input-
output matrices for 1992 through 1998. This allows us to obtain a more robust measure of
53
raw materials shares than that obtained using data for a single year. Although the input-
output matrices used do not cover our sample period, 1981-1991, we believe that input-
output relationships are relatively stable over these two decades. Matrix A has 22 rows
and 17 columns corresponding to the Colombian national accounts classification of
industries. The number of rows exceeds the number of columns because some raw
materials used in manufacturing originate in the primary sector. Note that by
22
construction a =1. Hence, our domestic raw materials price indexes are weighted
ij
i=1
averages of producer price indexes: for each manufacturing industry j = 1, ..., 17 and
22
time t, the domestic raw materials price index is defined as pRM = a
jt ijpit . To perform
i=1
this calculation we aggregate 29 manufacturing producer price indexes at the 3-digit ISIC
revision 2 into 17 producer price indexes at the broader Colombian national accounts
classification. We use production weights for the period 1975-1989 to aggregate these
price indexes. For the primary sectors included in the calculations we use wholesale price
indexes.
The construction of exports price indexes is more involved because the series
available from Banco de la República (Colombia's central bank) starts only in 1990. For the
period 1975-1990, we construct export price indexes using detailed trade data from the
Dirección de Impuestos y Aduanas Nacionales (DIAN). Export transactions in 1975-1990
are recorded at an 8-digit Colombian trade classification (NABANDINA) based on the
Brussels Tariff Nomenclature. For each NABANDINA and year, we compute export prices
in pesos per unit of weight by dividing the value of exports of each NABANDINA by its
weight. This is an imprecise proxy for unit export prices but is the best available because
54
only 5% of the observations have data on units other than weight. Note that even with better
information on units, the calculation can be subject to errors due to variation in the mix of
products included within each NABANDINA. To minimize potential spurious variation due
these measurement problems we follow two procedures. First, we remove from the
computations outliers defined as unit export prices whose average annual rate of growth
exceeds the 90th percentile or is less than the 10th percentile for the whole sample. Second,
we regress the log of the unit export price on a fixed NABANDINA effect, a set of time-
industry dummies, and a variable representing the deviation of each export price from the
law of one price. This variable is defined as log(EXPPESit/EXPDOLit) - log(Et), where
EXPPESit is the value of exports in pesos of NABANDINA i at time t, EXPDOLit is the
same value in dollars, and Et is the average exchange rate at time t. Since NABANDINA
positions with very small values of exports are more likely to be affected by measurement
problems, we estimate our regression using weighted least squares, with weights
proportional to the square root of the constant dollar value of exports. These regressions
generate predicted log unit export values for every NABANDINA and year with export data
(including positions excluded from the calculations due to outliers). We use these smoothed
unit export prices to compute Tornqvist price indexes for each ISIC industry j:
( )( )
I j
log pXjt - log pXjt-1 =0 .5 wit + wit-1 log pit - log pit-1 , where log pit is the estimated log
j j j j j
i=1
unit export price of NABANDINA i belonging to industry j at time t. The weights wit are j
the share of the value of exports in pesos of NABANDINA i in industry j at time t.
To obtain price indexes for imported raw materials, we first construct import price
indexes from the DIAN trade data, following the same procedure as for the export price
55
indexes. Then we follow a similar procedure to that used to construct domestic raw
materials price indexes, but instead of using general input-output matrices we use the 1994
Colombian input-output matrix for imported inputs.
B2. Capital stock and capital vintage
t-1
Our measure of gross capital is defined as Kit = FIRSTKi + (I i - Si ), where FIRSTKi is
=Fi
capital the plant had before its first year in the sample, Fi is the first year when plant i is in
the sample, Iit are purchases and Sit are sales of capital. Iit and Sit are obtained by summing,
respectively, purchases and sales of four different types of capital goods (buildings and
structures, machinery and equipment, transportation equipment, and office equipment)
expressed in constant pesos. We use the implicit price index for machinery and equipment
to deflate purchases and sales of office equipment since a separate price index for the latter
type of capital good is not available.
Our measure of capital vintage is the ratio of net capital to gross capital:
Vit = NKit Kit , where net capital is the conventional measure of capital obtained through
the permanent inventory method. More precisely, NKit = 4 j
j=1Kit , where j is a type of
capital good, and Kit is defined recursively as Kit = FIRSTKi
j j j for t = Fi , and
Kit = (1- d )Kit-1 + Iit - Sit for t > Fi . The depreciation rates used are taken from Pombo
j j j j j
(1999): 3.0% for buildings and structures, 7.7% for machinery and equipment, 11.9% for
transportation equipment, and 9.9% for office equipment. Our measure of vintage provides a
good summary of the temporal distribution of capital accumulation of a plant. While new
plants or plants that invest frequently will have higher values of Vit , plants that have not
56
invested for several years will have a low value of Vit , due to the effect of cumulative
depreciation in the plant's net capital stock.
3. Outliers
DANE conducts checks of the accuracy of the information provided by
manufacturing plants in the annual censuses, but there may still be some reporting errors.
While it is impossible for us to assess whether or not an outlier observation is due to a
reporting error, including outliers in the regressions can greatly distort the estimation of
the production function parameters and our measures of productivity. To avoid this risk,
we eliminate outliers from our dataset. To define outliers, we compute log differences
between four inputs (capital, labor, wage premium, and intermediate inputs) and output.
For each industry, we compute the first and third quartiles and the inter-quartile range
(IQR) of each of these log differences. We define an outlier as a plant for which in at
least one year one of the four log differences (a) exceeds the third quartile by x times the
IQR or more, or (b) is less than the first quartile by x times the IQR or more. The
threshold x is conventionally defined as 1.5, which corresponds to a 0.7% probability of
finding an outlier if the variable was normally distributed. To minimize the loss of data,
in nineteen out of twenty four industries we use a looser threshold of x = 2.5, which
corresponds to a 0.005% probability of finding an outlier under the assumption of
normality. In the remaining five industries, we apply a somewhat stricter threshold of x =
2.0 (corresponding to a 0.07% probability of finding an outlier under normality) since we
find that the presence of outliers in the capital stock variable leads to negative
coefficients for that variable under the looser 2.5 threshold.
57
Table A1: Production Function Coefficients - Selected Colombian Industries
Input OLS Levinsohn OLS Levinsohn Input OLS Levinsohn OLS Levinsohn
Petrin Petrin Petrin Petrin
(1) (2) (3) (4) (1) (2) (3) (4)
311 Food Products (1937 Obs.) 356 Plastics (914 Obs.)
Labor 0.137 *** 0.134 *** 0.149 *** 0.148 *** Labor 0.268 *** 0.263 *** 0.293 *** 0.295 ***
(0.007) (0.017) (0.007) (0.021) (0.015) -0.024 (0.014) (0.022)
Wage Premium 0.255 *** 0.289 *** Wage Premium 0.501 *** 0.424 ***
(0.026) (0.051) (0.046) (0.067)
Skill Intensity 0.233 *** 0.228 *** Skill Intensity 0.227 *** 0.146 **
(0.023) (0.042) (0.047) (0.070)
Intermediate Inputs 0.829 *** 0.601 *** 0.813 *** 0.785 *** Intermediate Inputs 0.705 *** 0.846 *** 0.681 *** 0.749 ***
(0.005) (0.087) (0.005) (0.054) (0.009) (0.058) (0.009) (0.060)
Capital 0.048 *** 0.221 *** 0.045 *** 0.051 * Capital 0.067 *** 0.024 0.055 *** 0.032 *
(0.005) (0.055) (0.004) (0.029) (0.007) (0.015) (0.006) (0.018)
Vintage 0.202 *** -0.060 Vintage 0.608 *** 0.284
(0.04) (0.138) (0.068) (0.190)
321 Textiles (997 Obs.) 381 Metal Products (1218 Obs.)
Labor 0.164 *** 0.165 *** 0.241 *** 0.234 *** Labor 0.277 *** 0.235 *** 0.334 *** 0.305 ***
(0.013) (0.025) (0.014) (0.022) (0.016) (0.028) (0.016) (0.034)
58 Wage Premium 0.352 *** 0.393 *** Wage Premium 0.453 *** 0.521 ***
(0.046) (0.059) (0.043) (0.080)
Skill Intensity 0.477 *** 0.428 *** Skill Intensity 0.519 *** 0.472 ***
(0.046) (0.070) (0.046) (0.107)
Intermediate Inputs 0.712 *** 0.540 *** 0.677 *** 0.482 *** Intermediate Inputs 0.698 *** 0.616 *** 0.657 *** 0.613 ***
(0.009) (0.068) (0.009) (0.087) (0.009) (0.063) (0.009) (0.086)
Capital 0.095 *** 0.180 *** 0.075 *** 0.015 Capital 0.053 *** 0.004 0.040 *** -0.056
(0.007) (0.058) (0.007) (0.024) (0.007) (0.04) (0.006) (0.095)
Vintage 0.485 *** 0.268 ** Vintage 0.391 *** 0.167 *
(0.071) (0.135) (0.066) (0.101)
322 Apparel (3045 Obs.)
Labor 0.335 *** 0.289 *** 0.384 *** 0.351 ***
(0.008) (0.017) (0.008) (0.015)
Wage Premium 0.437 *** 0.410 ***
(0.035) (0.05)
Skill Intensity 0.479 *** 0.489 ***
(0.03) (0.05)
Intermediate Inputs 0.639 *** 0.811 *** 0.605 *** 0.596 ***
(0.005) (0.044) (0.005) (0.055)
Capital 0.034 *** 0.041 ** 0.025 *** 0.029
(0.005) (0.019) (0.005) (0.025)
Vintage 0.089 ** 0.106
(0.037) (0.142)
Notes: Bootstrapped standard errors in parentheses in columns (2) and (4). ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels, respectively.